Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
The Internet today, has proven and since become an effective means to share information. There is also the consequent proliferation of adversaries who aim at unauthorized access to information being shared over the Internet medium. Most of these adversaries employ various methods, tools and techniques that are well-crafted to coordinate such attacks – which aim to deny services to authorized users as well as degrade system performance and service quality. The Distributed denial of service attacks have become a major threat to the information society and age in that they are carefully crafted attacks of large magnitude that possess the capability to wreak havoc at very high levels and national infrastructures. This study posits intelligent systems via the use of machine learning frameworks to detect such. We employ reinforcement deep learning method to distinguish between benign exchange of data and malicious attacks from data traffic. Results shows consequent success in the employment of deep learning neural network to effectively differentiate between acceptable and non-acceptable data packets (intrusion) on a network data traffic. (9 pt).
This paper presents the methodology of surface roughness inspection in CNC Turning process. Adaptive Neural Fuzzy Inference System classifier utilizes to predict the high accuracy roughness value with insisting of surface roughness image. The vision system captures the image and determines the mean value by using ANFIS algorithm. Training sets variables speed, depth of cut, feed rate and mean value are feed as the input and manual stylus probe surface roughness value feed as the output. After the simulation process, the testing input performed and finally getting the vision measurement value. This higher accuracy (above 95%) and low error rate (below 4%) can be achieved by using the ANFIS classifier, which is predominantly helpful for the industry to measure the surface roughness. Assign the quality of the product by evaluating the manual stylus probe and vision measurement value.